Mobile search based on predicted location

ABSTRACT

A method includes receiving one or more search terms at a mobile computing device while the mobile computing device is located at a particular location. A search query that includes the one or more search terms and a location history of the mobile computing device is transmitted to a server. The method also includes receiving one or more search results in response to the search query, where the one or more search results include content identified based on a predicted destination of the mobile computing device. An interface identifying the one or more search results is displayed at the mobile computing device.

BACKGROUND

Portable devices such as mobile phones are typically small, lightweight,and easily carried by users. As technology has advanced, such portabledevices have provided users with an increasing amount of “on-the-go”functionality. For example, portable devices can incorporate a digitalcamera, a media player, and Internet-surfing capabilities. Some portabledevices also include a global positioning system (GPS) transceiver. Theability to detect a current location of the portable device may enablelocation-based searches at the portable device. For example, a user of amobile phone may perform (e.g., via a browser or a maps application) asearch for restaurants that are near the current location. However, sucha listing may not always be useful. For example, if the user is in a carthat is traveling on a highway, identifying restaurants that the userhas already travelled past may not be useful.

Other devices, such as vehicle navigation systems, may identify pointsof interest that are along a route of the vehicle. However, the vehiclenavigation system may not be able to identify such points of interestunless a user has previously identified their intended destination.

SUMMARY

Systems and methods of performing a search at a mobile computing deviceand generating search results based on a predicted (e.g., future)location of the mobile computing device are disclosed. When a userenters one or more search terms into the mobile computing device, themobile computing device may transmit a search query that includes theone or more search terms and a location history of the mobile computingdevice to a server. For example, the location history may be used (e.g.,by a location prediction service accessible to the server) to predict afuture location of the mobile computing device. The predicted locationof the mobile computing device may be a predicted destination of themobile computing device, a point along a predicted route of the mobilecomputing device, or some other location. The search terms from the usermay be augmented with information regarding the predicted location,thereby creating a trajectory-aware search query. The trajectory-awaresearch query may be transmitted to a search engine to identifytrajectory-aware search results that may be more relevant to atravelling user than search results identified solely based on a currentlocation of the user.

In alternate implementations, the future location(s) of the mobilecomputing device may be predicted at the mobile computing device insteadof at a server or by a location prediction service. The mobile searchtechniques disclosed herein may identify trajectory-aware search resultseven when an intended destination or an intended route of the mobilecomputing device are not expressly input by a user. Thus, the disclosedsystems and methods may be operable “out of the box.”

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram to illustrate a particular embodiment of a system toperform mobile search based on a predicted location;

FIG. 2 is a diagram to illustrate another particular embodiment of asystem to perform mobile search based on a predicted location;

FIG. 3 is a diagram to illustrate a particular embodiment of a grid ofgeographic elements useable by the system of FIG. 1 or the system ofFIG. 2 ;

FIG. 4 is a diagram to illustrate a particular embodiment of a predictedroute and a predicted destination at the grid of FIG. 3 ;

FIG. 5 is a diagram to illustrate a particular embodiment of a displayedinterface at the mobile computing device of FIG. 1 or the mobilecomputing device of FIG. 2 ;

FIG. 6 is a flow diagram to illustrate a particular embodiment of amethod of performing mobile search based on a predicted location;

FIG. 7 is a flow diagram to illustrate another particular embodiment ofa method of performing mobile search based on a predicted location; and

FIG. 8 is a block diagram of a computing environment including acomputing device operable to support embodiments of computer-implementedmethods, computer program products, and system components as illustratedin FIGS. 1-7 .

DETAILED DESCRIPTION

In a particular embodiment, a method includes receiving one or moresearch terms at a mobile computing device while the mobile computingdevice is located at a particular location. The method also includestransmitting a search query to a server. The search query includes theone or more search terms and a location history of the mobile computingdevice. The method further includes receiving a search result inresponse to the search query, where the search result includes contentidentified based on a predicted destination of the mobile computingdevice. The method includes displaying an interface at the mobilecomputing device that identifies the search result.

In another particular embodiment, a non-transitory computer-readablestorage medium includes instructions that, when executed by a computer,cause the computer to receive a search query from a mobile computingdevice. The search query includes one or more search terms and a set ofgeographic elements traversed by the mobile computing device during atrip. The instructions are also executable to cause the computer todetermine a predicted location of the mobile computing device based onthe set of geographic elements and to create a trajectory-aware searchquery based on the one or more search terms and the predicted location.The instructions are further executable to cause the computer toidentify one or more search results in response to the trajectory-awaresearch query and to transmit the one or more search results to themobile computing device.

In another particular embodiment, a system includes a locationindicator, an input interface, an output interface, and a networkinterface. The system also includes a processor and a memory storinginstructions executable by the processor to track a location historybased on outputs of the location indicator. The instructions are alsoexecutable by the processor to receive one or more search terms via theinput interface and to predict a destination based on the locationhistory. The instructions are further executable to create atrajectory-aware search query based on the one or more search terms andthe predicted destination. The instructions are executable to transmitthe trajectory-aware search query to a search engine via the networkinterface and to receive one or more search results from the searchengine via the network interface. The instructions are also executableto display content associated with the one or more search results viathe output interface.

FIG. 1 is a diagram to illustrate a particular embodiment of a system100 to perform mobile search based on a predicted location. The system100 includes a server 130 communicatively coupled to a mobile computingdevice 110, a location prediction service 140, and a search engineserver 160. However, it should be noted that alternate implementationsmay include one or more of the server 130, the location predictionservice 140, and the search engine server 160 incorporated into a singledevice.

The mobile computing device 110 may be a mobile telephone, a smartphone,a portable digital assistant (PDA), a laptop computer, a tabletcomputer, a netbook computer, a navigation device, a digital camera, awatch or other electronic apparel or accessory, or some other portableelectronic device or portable computing device. The mobile computingdevice 110 may include one or more input and output interfaces ordevices. For example, the mobile computing device 110 may be a mobiletelephone that includes input devices such as a keypad and a microphone,output devices such as a speaker, and combination input/output devicessuch as a capacitive touchscreen. The mobile computing device 110 mayalso include a location indicator such as a GPS transceiver. Whensampled, the location indicator may return a current location of themobile computing device 110. In alternate embodiments, other forms oflocation measurement may be used by or provided to the mobile computingdevice 110. For example, the current location of the mobile computingdevice 110 may be determined from one or more wireless (e.g., Wi-Fi)access points, cellular towers, Bluetooth radios, or a separate locationdetermining device (e.g., a vehicle's navigation system).

In a particular embodiment, the mobile computing device 110 is operableto detect the start and stop of a trip made by a user that possesses themobile computing device 110. Trips may be detected automatically or inresponse to user input. For example, the start of a car trip may bedetected when the mobile computing device 110 experiences a sudden andlarge change in velocity. During the trip, the mobile computing device110 may sample the location indicator to maintain a location history 124of the mobile computing device 110. For example, the location history124 may identify a set of one or more geographic elements (e.g.,geographic cells, road segments, or other geographic elements) traversedby the mobile computing device 110 during the trip.

In a particular embodiment, the mobile computing device 110 samples thelocation indicator periodically (e.g., upon expiration of a samplingperiod such as 5 seconds). The mobile computing device 110 may identifya geographic element corresponding to the location returned by thelocation indicator and add the geographic element to the locationhistory 124. In a particular embodiment, the geographic element is notadded to the location history 124 unless the geographic element is morethan a threshold distance (e.g., 20 meters) from a previously returnedlocation. In alternate embodiments, a sampling period other than 5seconds and a threshold distance other than 20 meters may be used.

When the mobile computing device receives 110 a search query 120including one or more search terms 122 from the user, the mobilecomputing device 120 may add the location history 124 (or informationrelated to the location history 124) to the search query 120 beforetransmitting the search query 120 to the server 130. For example, thesearch terms 122 may be received via a browser application or a mapsapplication executing at the mobile computing device 110.

The server 130 may receive the search query 120 from the mobilecomputing device 110. In a particular embodiment, the server 130identifies a predicted location 142 of the mobile computing device 110after receiving the search query 120. For example, the predictedlocation 142 may be a predicted destination of the mobile computingdevice 110, a point on a predicted route of the mobile computing device110, or some other location. In certain situations, a predicteddestination of the mobile computing device 110 may correspond to thecurrent location of the mobile computing device 110 (i.e., the locationof the mobile computing device 110 when the search terms 122 wereentered). That is, the server 130 may predict that a user has arrived athis or her intended destination, will make a round-trip, or will nottravel anywhere else for a period of time. Alternately, the predicteddestination of the mobile computing device 110 may be different than thecurrent location of the mobile computing device 110.

The server 130 may add information regarding the predicted location 142to the search query 120 to generate a trajectory-aware search query 132.For example, the trajectory-aware search query 132 may include thesearch terms 122 and geographic coordinates or keywords corresponding tothe predicted location 142. To illustrate, if the search terms 122included “hardware store” and the predicted location was “Seattle,Wash.,” the trajectory-aware search query 132 may include “hardwarestore Seattle, Wash.” The trajectory-aware search query 132 may betransmitted to a search engine server 160 to generate the search results162. For example, the search engine server 160 may correspond to anInternet-accessible search engine.

In a particular embodiment, the server 130 utilizes a locationprediction service 140 to identify the predicted location 142. Forexample, the server 130 may extract the location history 124 from thesearch query 120 and may transmit the location history 124 to thelocation prediction service 140. The location prediction service 140 mayuse the location history 124 to determine the predicted location 142 andmay transmit the predicted location 142 to the server 130. For example,the location prediction service 140 may include or may have access tostored travel times 150 corresponding to a geographic region. The storedtravel times 150 may include estimates of how long it takes to travel(e.g., by foot, bicycle, motorcycle, car, public transportation, or anycombination thereof) between two points in the geographic region. Thelocation prediction service 140 may use the stored travel times 150 andother information or assumptions (e.g., that most trips are less thanhalf an hour in duration) to determine the predicted location 142. Anexemplary method of predicting a future location based on a locationhistory is described with reference to FIGS. 3-4 .

In a particular embodiment, the mobile computing device 110 transmitsupdated location histories to the server 130 during a trip. For example,an updated location history may indicate movement of the mobilecomputing device 110 from a first location to a second location. Theserver 130 may use the updated location histories to refine thepredicted location 142 and to identify updated search results via thesearch engine server 160. For example, the trajectory-aware search query132 may be transmitted to the search engine server 160 via a searchengine application programming interface (API). The server 130 maytransmit the updated search results to the mobile computing device 110,where the updated search results are displayed alongside or instead ofthe search results 162 (e.g., at a graphical user interface (GUI)generated by the mobile computing device 110). An exemplary searchinterface is further described with reference to FIG. 5 .

In another particular embodiment, trajectory-aware search is notperformed if the search terms 122 include a geographic search term. Toillustrate, if the search terms 122 or a second search query included“hardware store Portland, Oreg.” instead of merely “hardware store,” themobile computing device 120 may not add the location history 124 to thesearch query 120, since the search terms 122 already specify ageographic target. Alternately, the mobile computing device 110 may addthe location history 124, but the server 130 may ignore the locationhistory 124 and may transmit the search query 120 to the search engineserver 160 as-is to retrieve search results regarding hardware stores inthe Portland, Oreg. area (e.g., independently of a trajectory of themobile computing device 110).

In yet another embodiment, the location history 124 may be used fordisambiguation purposes. For example, if the search terms 122 included“hardware store Portland,” the server 130 may use the location history124 and the predicted location 142 to deduce that a mobile phone istravelling towards Portland, Oreg. and not Portland, Me. The server 130may thus prioritize search results associated with Portland, Oreg. overthose associated with Portland, Me. Alternately, or in addition, thelocation history 124 may be used to suggest auto-complete search terms.For example, if a user located in Bellevue, Wash. types “Kir” into asearch interface while the user is traveling from Bellevue, Wash.towards Kirkland, Wash., the search term “Kirkland” may be provided as asuggested auto-completion of the user's partially entered search query.

In another particular embodiment, the server 130 or the locationprediction service 140 may compute a speed of the mobile computingdevice 110. For example, each geographic element in the location history124 may have an associated timestamp representing when the mobilecomputing device 110 added the geographic element to the locationhistory 124. The speed of the mobile computing device 110 may becomputed based on the timestamps and may be used to augment thetrajectory-aware search query 132, refine the trajectory-aware searchresults 162, identify additional search results, or any combinationthereof. For example, a user may traverse the same sequence ofgeographic cells or road segments via a highway or via city streets. Thecomputed speed of the mobile computing device 110 may be greater whenthe user is on the highway than when the user is on city streets. Whenthe speed indicates that the user is on the highway (e.g., the speed isgreater than a threshold), search results close to or associated withone or more exits on the highway may be prioritized. In otherembodiments, a predicted location of a mobile computing device may alsobe used for non-search related purposes. For example, the predictedlocation may be used to determine traffic conditions en-route to thepredicted location, to suggest alternate routes to the predictedlocation, or for other non-search related purposes.

In operation, the mobile computing device 110 may receive the searchterms 122 and may generate the search query 120 based on the searchterms 122. The mobile computing device 110 may also add the locationhistory 124 to the search query 120 and transmit the search query 120with the location history 124 to the server 130. The server 130 mayidentify the predicted location 142 of the mobile computing device 110based on the location history 124. For example, the server 130 maytransmit the location history 124 to the location prediction service 140and receive the predicted location 142 from the location predictionservice 140. The server may augment the search terms 122 with thepredicted location 142 to generate the trajectory-aware search query132. The server may identify the one or more trajectory-aware searchresults 162 based on the trajectory-aware search query 132 (e.g., viathe search engine server 160). The server 130 may transmit thetrajectory-aware search results 162 to the mobile computing device 110.

It will be appreciated that the system 100 of FIG. 1 may improve themobile search experience in multiple ways. For example, the system 100of FIG. 1 may decrease the amount of time and/or number of searches auser performs before the user locates a desired point of interest. Asanother example, the system 100 of FIG. 1 may provide trajectory-awaresearch results that are not returned by conventional search techniques,thereby providing greater choice to the user. In addition, the system100 of FIG. 1 may provide mobile and localized search capabilities insituations where the user is unfamiliar with his or her surroundings.

It should be noted that although the system 100 of FIG. 1 depicts aseparate mobile computing device 110, server 130, location predictionservice 140, and search engine server 160, one or more components of thesystem 100 may be integrated into a single device. For example, FIG. 2illustrates a particular embodiment of a mobile computing device 200operable to perform mobile search based on a predicted location.

The mobile computing device 200 may integrate the functionality andoperations described with reference to the mobile computing device 100,the server 130, and the location prediction service 140 of FIG. 1 . Forexample, the mobile computing device 200 may include an input interface210, such as a touchscreen or keypad. The input interface 210 may beoperable to receive input 201 from a user. The mobile computing device200 may also include a location indicator 230 (e.g., a GPS transceiver).When sampled, the location indicator 230 may provide a location (e.g.,GPS coordinates) of the mobile computing device 200.

The mobile computing device 200 may further include a network interface250. For example, the network interface 250 may be a wireless interfacesuch as a cellular network interface or Wi-Fi network interface. Thenetwork interface 250 may be operable to communicate with network-basedentities such as an Internet-accessible search engine. In a particularembodiment, the mobile computing device 200 communicates with theInternet-accessible search engine via an API 202. The mobile computingdevice 200 may also include an output interface 260 operable to provideoutput 203 to a user. For example, the output interface 260 may be adisplay screen. In a particular embodiment, the input interface 210 andthe output interface 260 are integrated into a single device, such as acapacitive touchscreen.

The mobile computing device 200 may also include a trajectory-awaresearch and location prediction module 220. In a particular embodiment,the module 220 is implemented by processor-executable instructions. Forexample, such instructions may be stored at a memory of the mobilecomputing device 200 and may be executed by a processor of the mobilecomputing device 200. It should be noted that although thetrajectory-aware search and location prediction module 220 isillustrated in FIG. 2 as a single module, functionality of thetrajectory-aware search and location prediction module 220 may beimplemented using any number of hardware or software logic blocks ormodules.

During operation, the location indicator 230 may be sampled (e.g.,periodically) to track a location history 232 of the mobile computingdevice 200. The input interface 210 may receive the input 201 from auser, where the input 201 represents one or more search terms 212. Thesearch terms 212 may be input into the trajectory-aware search andlocation prediction module 220. The trajectory-aware search and locationprediction module 220 may determine a predicted location of the mobilecomputing device 200 based on the location history 232. In a particularembodiment, predicting the location includes reference to a database ortable of stored travel times 240. For example, the stored travel times240 may be stored in a non-volatile memory of the mobile computingdevice 200.

The module 220 may generate a trajectory-aware search query 222 thatincludes the predicted location. For example, the predicted location maybe a predicted destination of the mobile computing device 200 or may bea point on a predicted route of the mobile computing device 222. Thetrajectory-aware search query 222 may be transmitted to a search enginevia the network interface 250. The search engine may return one or moretrajectory-aware search results 224 that may be displayed to the uservia the output interface 260 as the output 203. For example, the searchresults 224 may be associated with the predicted location of the mobilecomputing device 200. Alternately, the trajectory-aware search query 222may be used to search one or more local data stores (e.g., localdatabases, files, or applications) at the mobile computing device 200instead of a search engine.

It will be appreciated that the system 200 of FIG. 2 may integratetrajectory-aware search functionality into a single device, such as amobile phone or a portable navigation system. The system 200 of FIG. 2may thus enhance user convenience and provide mobile device hardware orsoftware manufacturers with a differentiating location-based searchfeature.

FIGS. 3-4 illustrate a particular embodiment of a method of predicting alocation based on a location history. It should be noted that theillustrated location prediction method is for example only. Variouslocation prediction methods may be used in conjunction with thetrajectory-aware mobile search techniques disclosed herein.

In a particular embodiment, predicting locations based on locationhistories includes performing one or more initial (e.g., preparation)operations prior to making any predictions. A geographic area may bedivided into a plurality of geographic elements. For example, FIG. 3depicts a grid 300 divided into a plurality of equal-sized (e.g., 150meter×150 meter) geographic elements representing a portion of thegreater Seattle, Wash. area.

The grid 300 may include one or more navigable geographic elements 302and one or more non-navigable geographic elements 304. In a particularembodiment, the navigable geographic elements 302 represent geographicelements that are within a threshold distance (e.g., 150 meters) from anavigable roadway. When the grid 300 is divided into geographicelements, a corresponding set of geographic elements may be created. Asubset of the navigable geographic elements 302 may then be identified(e.g., by removing non-navigable geographic elements from the set) andtravel times may be determined between each pair of navigable geographicelements. The travel times may be stored for future reference at one ormore data storage devices (e.g., as the stored travel times 150 of FIG.1 or the stored travel times 240 of FIG. 2 ). In a particularembodiment, the number of calculations to be performed may be reduced bya factor of two by assuming that travel time between two geographiccells will be equal regardless of travel direction.

As a mobile computing device (e.g., the mobile computing device 100 ofFIG. 1 or the mobile computing device 200 of FIG. 2 ) traverses thegeographic elements of the grid 300, a location history of the mobilecomputing device may be tracked. For example, the location history mayrepresent the trajectory illustrated in FIG. 3 , where the trajectoryincludes an initial geographic element 310 (e.g., a start of a trip) anda most recent detected geographic element 320 (e.g., a currentlocation). The trajectory may be used to determine one or more of apredicted destination 402 and a predicted route 404 of the mobilecomputing device, as illustrated in FIG. 4 .

In a particular embodiment, one or more destination elements areidentified, where the destination elements are reachable within apredetermined time from the initial geographic element 310. For example,the predetermined time may be 30 minutes, based on an assumption or anempirical observation that most trips take less than 30 minutes tocomplete. The destination elements may be identified based on thepreviously stored travel times. For each such destination geographicelement, a probability of whether the destination geographic element isa future location (e.g., destination) of the mobile computing device maybe computed. Based on the computed probabilities, one or more of thepredicted destination 402 and the predicted route 404 may be determined.The predicted destination 402 and/or the predicted route 404 may be usedto identify trajectory-aware search results (e.g., search results nearthe predicted destination and/or along the predicted route 404).

In another particular embodiment, the predicted destination 402, thepredicted route 404, and the computed probabilities may be stored foreach user session to improve performance. Thus, when an updated locationhistory is received, the predicted destination 402 and predicted route404 may be updated without re-computing the probabilities. In anotherparticular embodiment, each geographic element in a location history maybe associated with a timestamp (e.g., representing when the mobilecomputing device entered, exited, or was located in the geographicelement). The timestamps may be used to compute a speed of the mobilecomputing device, and the computed speed may be used to identifyadditional trajectory-aware search results or may be used to prioritizeor refine previously identified search results. For example, when thecomputed speed is greater than a threshold (e.g., 55 miles per hour),search results associated with highway exits may be identified orprioritized.

It will be appreciated that the location prediction method of FIGS. 3-4may enable trajectory-aware search at mobile computing devices. It willalso be appreciated that the method of FIGS. 3-4 may protect userprivacy. For example, only those locations relevant to a current tripmay be tracked, and locations traversed during old trips may be deletedonce a trip has been completed. In addition, the method of FIGS. 3-4 mayoperate based on a broad resolution of 150 meter×150 meter areas insteadof tracking more precise addresses or coordinates visited by users.

FIG. 5 is a diagram to illustrate a particular embodiment of atrajectory-aware search interface 500. For example, the interface 500may be displayed by the mobile computing device 110 of FIG. 1 or themobile computing device 200 of FIG. 2 .

In a particular embodiment, the interface includes a search field 501and a search control 502. For example, the search field 501 may accepttext input corresponding to one or more search terms, and the searchcontrol 502 may be a button operable to submit the search query to atrajectory-aware search program. To illustrate, a user presented withthe interface 500 may enter a search term “pizza,” indicating that he orshe is interested in finding a pizza restaurant. The interface 500 mayalso include a map area 510. The map area 510 may depict a regionsurrounding a current location 511 of a mobile computing device. Theinterface 500 may also include a bulls-eye icon 503 operable to centerthe map area 510 on the current location 511 and a timer icon 504operable to view a history of previously conducted searches.Alternately, other icons or text-based controls may be displayed. In aparticular embodiment, the map area 510 is interactive and may supportpanning and zooming operations.

In response to a search query (e.g., “pizza”), one or more searchresults may be generated and displayed (e.g., overlaid) on the map area510. For example, the search results may be generated as described withreference to the search results 162 of FIG. 1 or the search results 224of FIG. 2 . The search results may include trajectory-aware searchresults, trajectory-unaware search results (e.g., results based on justthe term “pizza” and/or the current location 511 of the mobile computingdevice), or any combination thereof. To illustrate, trajectory-awaresearch results (e.g., a search result 512 “E”) may be denoted by a whiteflag at the interface 500, and trajectory-unaware search results (e.g.,a search result 515 “A”) may be denoted by a gray flag at the interface.

In a particular embodiment, each search result is interactive. Forexample, the user may select the search result 512 “E,” and theassociated flag may change from white to black, as illustrated at 513.In addition, an information box 514 may be displayed, where theinformation block 514 includes information associated with the locationor business corresponding to the selected search result 512 “E”. Forexample, for a pizza restaurant, the information box 514 may include aname, a price range, an address, and a phone number of the pizzarestaurant. In other embodiments, the information box 514 may alsoinclude or be operable (e.g., via selection) to view reviews of thepizza restaurant submitted by other users.

It should be noted that the retail (e.g., “hardware store”) andrestaurant (e.g., “pizza”) searches described herein are for exampleonly. The disclosed mobile search techniques may be applied to any othersearch domain. For example, trajectory-aware mobile search may beperformed on local (e.g., city, state, or municipal) content, webcontent, advertising, traffic information, friends or friend groups(e.g., in social networks), and other information domains. Moreover,trajectory information may not only be used to identify search results,but also to prioritize or rank search results, to compute informationrelated to the trajectory (e.g., a remaining time to arrival at thepredicted destination), and to pre-fetch search results or contentlikely to be retrieved by a user during subsequent searches orInternet-browsing sessions.

FIG. 6 is a flow diagram to illustrate a particular embodiment of amethod 600 of performing mobile search based on predicted location. Inan illustrative embodiment, the method 600 may be performed at themobile computing device 110 of FIG. 1 or the mobile computing device 200of FIG. 2 .

The method 600 may include detecting a start of a trip by a mobilecomputing device, at 602. For example, in FIG. 2 , the trajectory-awaresearch and location prediction module 220 may detect the start of atrip. The method 600 may also include maintaining a location history ofthe mobile computing device, at 604. In a particular embodiment,maintaining the location history includes performing an iterativeprocess. The process may include sampling a location indicator, at 606.For example, the location indicator may be sampled every five seconds.When it is determined, at 608, that a location returned by the sampleindicator is a new location, the process may include adding a geographicelement corresponding to the returned location to a set of geographicelements, at 610. When the returned location is not a new location, theprocess may iterate back to 606. The location history may continue to betracked until the trip ends, at which point the location history may bedeleted. In other embodiments, the location histories may be saved(e.g., on a per-user basis).

The method 600 may further include receiving one or more search terms atthe mobile computing device while the mobile computing device is at aparticular location, at 612. For example, in FIG. 2 , the search terms212 may be received via the input interface 210. The method 600 mayinclude predicting a location of the mobile computing device based onthe location history, at 614. The predicted location may be a predicteddestination of the mobile computing device or a point along a predictedroute of the mobile computing device. For example, in FIG. 2 , thetrajectory-aware search and location prediction module 220 may determinethe predicted location based on the location history 232.

The method 600 may also include creating a trajectory-aware search querybased on the one or more search terms and the predicted location, at616. For example, in FIG. 2 , the trajectory-aware search and locationprediction module 220 may create the trajectory-aware search query 222.The method 600 may further include receiving a search result or searchresults in response to the search query, at 618. The search resultincludes content identified based on the predicted location. Forexample, in FIG. 2 , the search results 224 may be received via thenetwork interface 250.

The method 600 may include displaying an interface at the mobilecomputing device that identifies the search result, a 620. For example,in FIG. 2 , the interface may be displayed via the output interface 260.In an illustrative embodiment, the displayed interface is the interface500 of FIG. 5 .

FIG. 7 is a flow diagram to illustrate another particular embodiment ofa method 700 of performing mobile search based on a predicted location.In a particular embodiment, the method 700 may be performed at thesystem 100 of FIG. 1 and is illustrated with reference to FIGS. 3-4 .

The method 700 may include dividing a geographic area into a pluralityof geographic elements, at 702. For example, in FIG. 1 , the locationprediction service 140 may divide a geographic area into a plurality ofgeographic elements. To illustrate, the geographic area may be dividedinto the grid 300 of FIG. 3 .

The method 700 may also include identifying a subset of navigablegeographic elements that are within a threshold distance of a navigableroadway, at 704. For example, in FIG. 1 , the location predictionservice 140 may identify a subset of geographic elements. To illustrate,the subset of navigable geographic elements may be the subset ofnavigable geographic elements 302 of FIG. 3 .

The method 700 may further include determining and storing travel timesbetween each pair of navigable geographic elements, at 706. For example,in FIG. 1 , the location prediction service may generate the storedtravel times 150. The method 700 may include receiving a search queryfrom a mobile computing device, at 708. The search query may include oneor more search terms and a set of geographic elements traversed by themobile computing device during a trip. For example, in FIG. 1 , theserver 130 may receive the search query 120 from the mobile computingdevice 110, where the search query 120 includes the search terms 122 andthe location history 124. In an illustrative embodiment, the locationhistory 124 includes a set of geographic elements traversed by themobile computing device 110 during the trip. The method 700 may alsoinclude identifying one or more destination geographic elements that arereachable from an initial geographic element, at 710. For example,referring to FIG. 3 , the initial geographic element may be the initialgeographic element 310 of FIG. 3 .

The method 700 may further include computing probabilities of whetherthe destination geographic elements are a destination of the mobilecomputing device, at 712, and determining a predicted destination basedon the probabilities, at 714. For example, referring to FIG. 4 thepredicted destination may be the predicted destination 402. Alternately,a point on the predicted route 404 may be determined. In a particularembodiment, the probabilities are computed based on an algorithm thatrelies on an assumption that travelers typically choose at leastmoderately efficient routes to a desired destination. Based on a user'strajectory (e.g., location history), the algorithm may compute howefficiently the user has approached or is approaching each potentialdestination (e.g., cell in the grid 300 of FIG. 3 ). The efficienciesmay be used to compute the probability that each potential destinationis the intended destination of the user. In some embodiments, a Markovmodel may be used during execution of the prediction algorithm. However,it should be noted that other models or other location predictionalgorithms may also be used.

The method 700 may include creating a trajectory-aware search querybased on the one or more search terms and the predicted destination, at716, and identifying one or more search results in response to thetrajectory-aware search query, at 718. For example, in FIG. 1 , theserver 130 may create the trajectory-aware search query 132 and mayreceive the search results 162. The method 700 may further includetransmitting the one or more search results to the mobile computingdevice, at 720. For example, in FIG. 1 , the server 130 may transmit thesearch results 162 to the mobile computing device 110. All or a portionof the method 700 may be re-executed to update the search results (e.g.,as the user travels towards the predicted location or performsadditional searches).

FIG. 8 shows a block diagram of a computing environment 800 including acomputing device 810 operable to support embodiments ofcomputer-implemented methods, computer program products, and systemcomponents according to the present disclosure. For example, thecomputing device 810 or components thereof may include, implement, or beincluded as a component of the mobile computing device 110 of FIG. 1 ,the server 130 of FIG. 1 , the location prediction service 140 of FIG. 1, the mobile computing device 200 of FIG. 2 , the search engine server160, or any portions thereof.

The computing device 810 includes at least one processor 820 and asystem memory 830. For example, the computing device 810 may be adesktop computer, a laptop computer, a tablet computer, a mobile phone,a server, or any other fixed or mobile computing device. Depending onthe configuration and type of computing device, the system memory 830may be volatile (such as random access memory or “RAM”), non-volatile(such as read-only memory or “ROM,” flash memory, and similar memorydevices that maintain stored data even when power is not provided),non-transitory, some combination of the three, or some other memory. Thesystem memory 830 may include an operating system 832, one or moreapplication platforms 834, one or more applications 836, and programdata 838. In the embodiment illustrated, the system memory 830 includesa trajectory-aware search and location prediction module 837. In anillustrative embodiment, the trajectory-aware search and locationprediction module 837 is the trajectory-aware search and locationprediction module 220 of FIG. 2 or is incorporated into one or more ofthe mobile computing device 110, the server 130, and the locationprediction service 140 of FIG. 1 .

The computing device 810 may also have additional features orfunctionality. For example, the computing device 810 may also includeremovable and/or non-removable additional data storage devices such asmagnetic disks, optical disks, tape, and memory cards. Such additionalstorage is illustrated in FIG. 8 by removable storage 840 andnon-removable storage 850. Computer storage media may include volatileand/or non-volatile storage and removable and/or non-removable mediaimplemented in any technology for storage of information such ascomputer-readable instructions, data structures, program components orother data. The system memory 830, the removable storage 840, and thenon-removable storage 850 are all examples of computer storage media.The computer storage media includes, but is not limited to, RAM, ROM,electrically erasable programmable read-only memory (EEPROM), flashmemory or other memory technology, compact disks (CD), digital versatiledisks (DVD) or other optical storage, magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices, or any othermedium that can be used to store information and that can be accessed bythe computing device 810. Any such computer storage media may be part ofthe computing device 810. In an illustrative embodiment, such computerstorage media is used to store the stored travel times 150 of FIG. 1 orthe stored travel times 240 of FIG. 2 .

The computing device 810 may also have input device(s) 860, such as akeyboard, mouse, pen, voice input device, touch input device, etc.connected via one or more input interfaces. In an illustrativeembodiment, the input device(s) 860 include the input interface 210 ofFIG. 2 . Output device(s) 870, such as a display, speakers, printer,etc. may also be included and connected via one or more outputinterfaces. In an illustrative embodiment, the output device(s) 870include the output interface 260 of FIG. 2 .

The computing device 810 also contains one or more communicationconnections 880 that allow the computing device 810 to communicate withother computing devices over a wired or a wireless network. In anillustrative embodiment, the communication connections 880 include thenetwork interface 250 of FIG. 2 . The other computing devices mayinclude one or more of a mobile computing device 892 (e.g., the mobilecomputing device 100 of FIG. 1 or the mobile computing device 200 ofFIG. 2 ), a location prediction service 894 (e.g., the locationprediction service 140 of FIG. 1 ), and a search engine 896 (e.g., thesearch engine server 160 of FIG. 1 ). For example, the one or morecommunication connections 880 may represent an interface thatcommunicates with other computing devices via a network.

It will be appreciated that not all of the components or devicesillustrated in FIG. 8 or otherwise described in the previous paragraphsare necessary to support embodiments as herein described. For example,the removable storage 840 may be optional.

The illustrations of the embodiments described herein are intended toprovide a general understanding of the structure of the variousembodiments. The illustrations are not intended to serve as a completedescription of all of the elements and features of apparatus and systemsthat utilize the structures or methods described herein. Many otherembodiments may be apparent to those of skill in the art upon reviewingthe disclosure. Other embodiments may be utilized and derived from thedisclosure, such that structural and logical substitutions and changesmay be made without departing from the scope of the disclosure.Accordingly, the disclosure and the figures are to be regarded asillustrative rather than restrictive.

Those of skill would further appreciate that the various illustrativelogical blocks, configurations, modules, and process steps orinstructions described in connection with the embodiments disclosedherein may be implemented as electronic hardware or computer software.Various illustrative components, blocks, configurations, modules, orsteps have been described generally in terms of their functionality.Whether such functionality is implemented as hardware or softwaredepends upon the particular application and design constraints imposedon the overall system. Skilled artisans may implement the describedfunctionality in varying ways for each particular application, but suchimplementation decisions should not be interpreted as causing adeparture from the scope of the present disclosure.

The steps of a method described in connection with the embodimentsdisclosed herein may be embodied directly in hardware, in a softwaremodule executed by a processor, or in a combination of the two. Asoftware module may reside in computer readable media, such as randomaccess memory (RAM), flash memory, read only memory (ROM), registers, ahard disk, a removable disk, a CD-ROM, or any other form of storagemedium known in the art. An exemplary storage medium is coupled to aprocessor such that the processor can read information from, and writeinformation to, the storage medium. In the alternative, the storagemedium may be integral to the processor or the processor and the storagemedium may reside as discrete components in a computing device orcomputer system.

Although specific embodiments have been illustrated and describedherein, it should be appreciated that any subsequent arrangementdesigned to achieve the same or similar purpose may be substituted forthe specific embodiments shown. This disclosure is intended to cover anyand all subsequent adaptations or variations of various embodiments.

The Abstract is provided with the understanding that it will not be usedto interpret or limit the scope or meaning of the claims. In addition,in the foregoing Detailed Description, various features may be groupedtogether or described in a single embodiment for the purpose ofstreamlining the disclosure. This disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter may be directed toless than all of the features of any of the disclosed embodiments.

The previous description of the embodiments is provided to enable aperson skilled in the art to make or use the embodiments. Variousmodifications to these embodiments will be readily apparent to thoseskilled in the art, and the generic principles defined herein may beapplied to other embodiments without departing from the scope of thedisclosure. Thus, the present disclosure is not intended to be limitedto the embodiments shown herein but is to be accorded the widest scopepossible consistent with the principles and novel features as defined bythe following claims.

1. (canceled)
 2. A method comprising: receiving a search query from acomputing device located in a geographic region; detecting that thesearch query includes an ambiguous location; based on the search queryincluding an ambiguous location, predicting a location based on alocation history associated with the computing device; performing asearch based on the search query; prioritizing search results associatedwith the predicted location over search results not associated with thepredicted location; and causing presentation of the prioritized searchresults on the computing device.
 3. The method of claim 2, furthercomprising: augmenting the search query with the predicted locationprior to performing the search.
 4. The method of claim 2, wherein thepredicted location is at least partly based on a travel time estimate,the travel time estimate based on a time to travel to the predictedlocation.
 5. The method of claim 2, wherein the predicting the locationcomprises: identifying one or more destination elements in thegeographic region; determining probabilities that the one or moredestination elements are a future location of the computing device; andidentifying the predicted location based on the probabilities.
 6. Themethod of claim 5, wherein the one or more destination elements areidentified based on being reachable from a current location of thecomputing device within a predetermined amount of time.
 7. The method ofclaim 2, wherein the predicted location comprises a current location ofthe computing device.
 8. The method of claim 2, further comprising:based on the location history, predicting a route of the computingdevice, wherein the predicted location comprises a point on thepredicted route of the computing device.
 9. The method of claim 8,further comprising: augmenting the search query with informationregarding the predicted route to generate a trajectory-aware searchquery, wherein the search is performed using the trajectory-aware searchquery.
 10. The method of claim 8, further comprising: based on thepredicted route, pre-fetching content likely to be retrieved by a userof the computing device.
 11. A system comprising: one or more hardwareprocessors; and a memory device storing instructions that, when executedby the one or more hardware processors, cause the one or more hardwareprocessors to perform operations comprising: receiving a search queryfrom a computing device located in a geographic region; detecting thatthe search query includes an ambiguous location; based on the searchquery including an ambiguous location, predicting a location based on alocation history associated with the computing device; performing asearch based on the search query; prioritizing search results associatedwith the predicted location over search results not associated with thepredicted location; and causing presentation of the prioritized searchresults on the computing device.
 12. The system of claim 11, wherein theoperations further comprise: augmenting the search query with thepredicted location prior to performing the search.
 13. The system ofclaim 11, wherein the predicted location is at least partly based on atravel time estimate, the travel time estimate based on a time to travelto the predicted location.
 14. The system of claim 11, wherein thepredicting the location comprises: identifying one or more destinationelements in the geographic region; determining probabilities that theone or more destination elements are a future location of the computingdevice; and identifying the predicted location based on theprobabilities.
 15. The system of claim 14, wherein the one or moredestination elements are identified based on being reachable from acurrent location of the computing device within a predetermined amountof time.
 16. The system of claim 11, wherein the predicted locationcomprises a current location of the computing device.
 17. The system ofclaim 11, wherein the operations further comprise: based on the locationhistory, predicting a route of the computing device, wherein thepredicted location comprises a point on the predicted route of thecomputing device.
 18. The system of claim 17, wherein the operationsfurther comprise: augmenting the search query with information regardingthe predicted route to generate a trajectory-aware search query, whereinthe search is performed using the trajectory-aware search query.
 19. Thesystem of claim 17, wherein the operations further comprise: based onthe predicted route, pre-fetching content likely to be retrieved by auser of the computing device.
 20. A storage medium storing instructionsthat, when executed by one or more hardware processors, causes the oneor more hardware processors to perform operations comprising: receivinga search query from a computing device located in a geographic region;detecting that the search query includes an ambiguous location; based onthe search query including an ambiguous location, predicting a locationbased on a location history associated with the computing device;performing a search based on the search query; prioritizing searchresults associated with the predicted location over search results notassociated with the predicted location; and causing presentation of theprioritized search results on the computing device.
 21. The storagemedium of claim 20, wherein the predicting the location comprises:identifying one or more destination elements in the geographic region;determining probabilities that the one or more destination elements area future location of the computing device; and identifying the predictedlocation based on the probabilities.